Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds

Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine lea...

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Bibliographic Details
Published in:Foods 2025-01, Vol.14 (2), p.169
Main Authors: Huang, Jing, Zhang, Xuenan, Yang, Hang, Li, Zhenbiao, Xue, Zhengfang, Wang, Qingqing, Zhang, Xinyuan, Ding, Shenghua, Luo, Zisheng, Xu, Yanqun
Format: Article
Language:English
Subjects:
Accuracy
Classification
Datasets
Decision making
Decision trees
Discriminant analysis
Fruits
Gas chromatography
Ionic mobility
Libraries
Machine learning
Maturity
Neural networks
Nitrogen
Organic compounds
Principal components analysis
Spectrometry
Strawberries
strawberry
Supply chains
Support vector machines
variety
VOCs
Volatile organic compounds
Workflow
Citations: Items that this one cites
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Summary:Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification. A comprehensive VOC dataset was rapidly collected using gas chromatography-ion mobility spectrometry (GC-IMS) from five strawberry varieties at four maturity stages (n = 300) and visualized through principal component analysis (PCA). Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). The accuracy of all models ranged from 90.00% to 98.33%, with the NN model demonstrating the best performance. Specifically, it achieved 96.67% accuracy for single-maturity classification, 98.33% for single-variety classification, and 96.67% for dual maturity and variety classification, along with 98.09% precision, 97.92% recall, and 97.91% F1 score. Feature importance analysis indicated that the NN model exhibited the most balanced reliance on various VOCs, contributing to its optimal performance with the broad-spectrum VOC detection method, GC-IMS. Overall, these findings underscore the potential of NN modeling for accurate and efficient fruit classification based on integrated VOC profiles.
ISSN:2304-8158
2304-8158
DOI:10.3390/foods14020169